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cs.LG2025
On the Depth of Monotone ReLU Neural Networks and ICNNs
Egor Bakaev, Florestan Brunck, Christoph Hertrich +2
We study two models of ReLU neural networks: monotone networks (ReLU) and input convex neural networks (ICNN). Our focus is on expressivity, mostly in terms of depth, and we pr…
cs.LG2024
Depth Separations in Neural Networks: Separating the Dimension from the Accuracy
Itay Safran, Daniel Reichman, Paul Valiant
We prove an exponential size separation between depth 2 and depth 3 neural networks (with real inputs), when approximating a -Lipschitz target function to constant…